Summary

Project summary

Goals:

Findings:

Data Overview

Twitter data

Table of data

Here is a sample of the type of twitter information we obtained.

created_at tweet_id full_text user_id user_location geo_type geo_coordinates language retweet_count favorite_count lat lon
Sat Apr 16 23:10:06 +0000 2016 7.214758e+17 Don’t forget! argonautoysterbar every Monday night at @SBWineCollectiv! Who says you can’t… https://t.co/wv15dOudf5 2683395770 131 Anacapa Street, Suite C Point c(34.41492371, -119.69080081) en 0 0 34.41492 -119.6908
Mon Jul 18 16:41:31 +0000 2016 7.550801e+17 Come in this week for our newest espresso offering! Introducing Finca Chayote from Costa Rica.… https://t.co/75RR41IzV0 79255524 Santa Barbara Point c(34.418833, -119.695292) en 0 1 34.41883 -119.6953
Fri Dec 11 01:22:31 +0000 2015 6.751235e+17 #ChristmasLights @ The Mesa https://t.co/mUbWlkA4Zy 598602591 Santa Barbara Point c(34.4051208, -119.7215424) en 0 0 34.40512 -119.7215
Mon Sep 03 23:10:37 +0000 2018 1.036753e+18 Good morning pages. Thank you. Thank you. Thank you. Overwhelmed with gratitude for this glorious weekend of so much love, adventure, and blessings. 💖 #createyourbestlife @ Santa Barbara,… https://t.co/JEhtKiqrSE 195250327 Los Angeles, CA Point c(34.4258, -119.714) en 0 0 34.42580 -119.7140
Sun Mar 15 02:17:47 +0000 2015 5.769303e+17 Update: It actually gets better http://t.co/bnZFQpnDHm 33143542 San Francisco, CA Point c(34.40927524, -119.85860724) en 0 1 34.40928 -119.8586
Sun Jun 04 06:16:28 +0000 2017 8.712493e+17 Going up the coast next weekend to crack some cold ones with the boys. @Fallujah_71 is taking… https://t.co/wOX67kbMKj 3165777576 Los Angeles, CA Point c(34.4258, -119.714) en 0 0 34.42580 -119.7140
Sat May 09 20:00:16 +0000 2015 5.971290e+17 VACATION!!! (@ Fess Parker’s Doubletree Resort in Santa Barbara, CA) https://t.co/2i3nQpmc7s 58954631 NA Point c(34.41671154, -119.67623413) fr 0 0 34.41671 -119.6762
Mon Dec 12 04:51:28 +0000 2016 8.081724e+17 Sixth and hill links. You most likely need a Cuban for Christmas or the new year. . Wolf’s… https://t.co/W4xp4ncxvM 246578185 Santa Barbara, CA Point c(34.4166923, -119.6951291) en 0 0 34.41669 -119.6951
Fri Jul 29 20:47:49 +0000 2016 7.591283e+17 Look who’s on the train coming to meet us in Ventura! #ezekiel #auggie bnoblelindsay @ Santa… https://t.co/oBqmMSHMIN 37075938 San Diego Point c(34.41387307, -119.68731051) en 0 1 34.41387 -119.6873
Sat Apr 04 18:39:22 +0000 2015 5.844250e+17 Spring has sprung. Everyone is making the chop ✂️ #lob #ShortHairClub #stylist #cutoftheyear @ The… https://t.co/ECntwsuGua 867406032 Santa Barbara, California Point c(34.421415, -119.656332) en 0 0 34.42142 -119.6563

Caveats

Required crimson hexagon access

Maps

Interactive with cluster markers

As you zoom in on the map, clusters will disaggregate. You can click on blue points to see the tweet.

Tweet density

This is log-transformed. There is a single coordinate that has over 11,000 tweets reported across all years. It is near De La Vina between Islay and Valerio. There is nothing remarkable about this site so I assume it is the default coordinate when people tag “Santa Barbara” generally. The coordinate is 34.4258, -119.714.

Identifying tourists and locals

If the user has self-identified their location as somewhere in the Santa Barbara area, they are designated a local. This includes Carpinteria, Santa Barbara, Montecito, Goleta, Gaviota and UCSB. For the remainder, we use the number of times they have tweeted from Santa Barbara within a year to designate user type. If someone has tweeted across more than 2 months in the same year from Santa Barbara, they are identified as a local. This is consistent with how Eric Fischer determined tourists in his work. This is not fool-proof and there are instances were people visit and tweet from Santa Barbara more than two months a year, especially if they are visiting family or live within a couple hours driving distance.

There are 26408 tweets from tourists and 56468 tweets from locals.

Identifying nature-based tweets

Applying dictionary

The dictionary is “nature-based” and is a list of words I put together. I had a hard time finding an ontology or lexicon that would fit this project. These are definitely skewed more towards nature and recreation rather than words like “home” or “connection”.

##  [1] "hike"        "trail"       "hiking"      "camping"     "tent"       
##  [6] "climb"       "summit"      "fishing"     "sail"        "sailing"    
## [11] "boat"        "boating"     "ship"        "cruise"      "cruising"   
## [16] "bike"        "biking"      "dive"        "diving"      "surf"       
## [21] "surfing"     "paddle"      "swim"        "ocean"       "beach"      
## [26] "^sea"        "sand"        "coast"       "island"      "wave"       
## [31] "fish"        "whale"       "dolphin"     "pacific"     "crab"       
## [36] "lobster"     "water"       "shore"       "marine"      "seawater"   
## [41] "lagoon"      "slough"      "saltwater"   "underwater"  "tide"       
## [46] "aquatic"     "^tree"       "^earth"      "weather"     "sunset"     
## [51] "sunrise"     "^sun"        "climate"     "park"        "wildlife"   
## [56] "^view"       "habitat"     "^rock"       "nature"      "mountains"  
## [61] "^peak"       "canyon"      "pier"        "wharf"       "environment"
## [66] "ecosystem"

Where are nature-based tweets?

Are tweets in protected areas more often nature-based?

California Protected Areas Database

Time

Timeline of tweets

Initial hypothesis was identifying spikes in nature-based tweets around three significant events: - Refugio oil spill in 2015 - Thomas fire in 2017 - Debris flow in 2018

Word clouds

top 100 words for locals vs tourist. And we could do this in space. At sterns wharf what are people tweeting about? At Elings, what are locals tweeting about?

Maybe in word clouds we can see some changes due to natural events

All of SB

By area

Sentiment Analysis

Summary

Lessons learned

Data is harder to find

Future research

Looking at different scale areas

There might be an interesting comparison between rural-suburban-urban areas. We hypothseize that the tourist/local alignment would split in urban areas, maybe aligned in suburban (like SB) and maybe not exist in rural.

Proportion of words that are nature based tells you how people. In Santa Barbara, there will be a lot of nature-based sense of place. In Manhattan, we wouldn’t expect to see nature based ones so much.

In a blog piece we can pose questions that we couldn’t answer but stuff like “can proportion of tourists/locals in place engagement tell us anything”.

Could compare % nature based tweets in SB to other areas. If we did this across the whole state, what proportion% are nature based? Maybe on average its just 5%.

Where and why do locals and tourists overlap in their use of area. SB seems to have a high alignment of tourists/locals, which may be helpful for local policy. Maybe places with distinct differences in how tourists/locals use places.

Look at cities of different coastal sizes rural - small town - urban - mega city. Could see how tourists/locals patterns differentiate across scale.

Is there a threshold of tourists where locals don’t go anymore?

In areas where we see both tourists and locals engaging, what characteristics do we see?

Quantifying transitions between rural to city.